LLM-as-an-Augmentor: Improving the Data Augmentation for Aspect-Based Sentiment Analysis with Large Language Models

Aspect-Based Sentiment Analysis ABSA is a vital fine-
grained sentiment analysis task that aims to determine the sentiment po-
larity towards an aspect in a sentence. Due to the expensive and limited
amounts of labeled data, data augmentation DA methods have become
the de-facto standard for ABSA. However, current DA methods usually
suffer from 1 poor fluency and coherence and 2 lack of the diversity of
generated data. To this end, we propose a simple-yet-effective DA method
for ABSA, namely LLM-as-an-Augmentor, which leverages the pow-
erful capability of third-party larger language models LLMs to improve
the quality of generated data. Specifically, we introduce several text re-
construction strategies and use them to guide the LLMs for automatic
data generation via a carefully-designed prompting method. Extensive
experiments on 5 baseline methods and 3 widely-used benchmarks show
that our LLM-as-an-Augmentor can bring consistent and significant
performance gains among all settings. More encouragingly, given only
15 labeled data, our method can achieve comparable performance to
that of full labeled data.